Aetiology and epidemiology of surgical vitreoretinal presentations in an Australian paediatric population: A seven-year retrospective study.

BACKGROUND: Paediatric vitreoretinal pathology is distinct from adult cases in both presentation and surgical planning. Here we aim to report the aetiology and epidemiology in children 0-18 years requiring vitreoretinal surgery at a major tertiary paediatric hospital in Queensland, Australia. METHODS: Retrospective review of cases requiring vitreoretinal surgery between May 2015 and October 2022 was conducted. Demographics, ocular and medical history, surgical pathology, procedures performed, and epidemiology data were retrieved. Patients were grouped into three main aetiologies: traumatic, syndromic, or secondary. RESULTS: A total of 124 patients, the majority male (87, 70.2%) with a mean age of 10.3 years underwent vitreoretinal surgery. Trauma accounted for 32.3% of cases requiring surgery of which 47% were due to a penetrating eye injury. 35.5% were associated with a syndromic cause with common aetiology including coats, congenital cataract, sticklers, and retinopathy of prematurity. 32.3% developed secondary pathology and retinal detachment was the primary cause for surgery (55%). The average time from symptom onset to presentation was 30 days (SD 56.88) with patients living an average of 306.2 km (SD 558.9) away from the Queensland Children's Hospital. Older age was significantly associated with increased days to presentation in the traumatic group (p < 0.05). CONCLUSIONS: This study provides an insight into the aetiology and epidemiology of paediatric vitreoretinal presentations in Queensland, Australia.

Idiopathic full-thickness macular hole in a pediatric patient: case report and long-term follow-up.

Pediatric full-thickness macular holes (FTMHs) are uncommon; the majority are the result of direct ocular trauma. Idiopathic FTMHs are even rarer and have only been reported in the literature as individual cases. The clinical course and prognosis of FTMHs remains largely unknown, and challenges in management such as deprivational amblyopia must be considered. We present a case of an idiopathic FTMH in a child with 20/32 visual acuity who did not undergo surgical intervention. Unlike the natural progression of idiopathic FTMHs seen in adults, there was no progression in size of the FTMH during 1 year of follow-up and no deterioration in vision. This may support conservative management in certain pediatric cases.

Multicenter Validation of Deep Learning Algorithm ROP.AI for the Automated Diagnosis of Plus Disease in ROP.

Purpose: Retinopathy of prematurity (ROP) is a sight-threatening vasoproliferative retinal disease affecting premature infants. The detection of plus disease, a severe form of ROP requiring treatment, remains challenging owing to subjectivity, frequency, and time intensity of retinal examinations. Recent artificial intelligence (AI) algorithms developed to detect plus disease aims to alleviate these challenges; however, they have not been tested against a diverse neonatal population. Our study aims to validate ROP.AI, an AI algorithm developed from a single cohort, against a multicenter Australian cohort to determine its performance in detecting plus disease. Methods: Retinal images captured during routine ROP screening from May 2021 to February 2022 across five major tertiary centers throughout Australia were collected and uploaded to ROP.AI. AI diagnostic output was compared with one of five ROP experts. Sensitivity, specificity, negative predictive value, and area under the receiver operator curve were determined. Results: We collected 8052 images. The area under the receiver operator curve for the diagnosis of plus disease was 0.75. ROP.AI achieved 84% sensitivity, 43% specificity, and 96% negative predictive value for the detection of plus disease after operating point optimization. Conclusions: ROP.AI was able to detect plus disease in an external, multicenter cohort despite being trained from a single center. Algorithm performance was demonstrated without preprocessing or augmentation, simulating real-world clinical applicability. Further training may improve generalizability for clinical implementation. Translational Relevance: These results demonstrate ROP.AI's potential as a screening tool for the detection of plus disease in future clinical practice and provides a solution to overcome current diagnostic challenges.

Proliferative Vitreoretinopathy Following Transscleral Diode Cyclophotocoagulation.

PRCIS: Transscleral diode laser cyclophotocoagulation may trigger the development of proliferative vitreoretinopathy. Our article demonstrates one such case leading to tractional macula-off retinal detachment in a child with aphakic glaucoma. PURPOSE: The purpose of this article is to describe a case of proliferative vitreoretinopathy (PVR) developing subsequent to transscleral diode laser cyclophotocoagulation (cyclodiode) in a pediatric patient with aphakic glaucoma. PVR most commonly occurs following rhegmatogenous retinal detachment repair; however, to the best of our knowledge, it has never been reported to appear after cyclodiode. METHODS: Retrospective evaluation of case presentation and intraoperative findings. RESULTS: A 13-year-old girl with aphakic glaucoma presented 4 months after cyclodiode of the right eye with a retrolental fibrovascular membrane and anterior PVR. The PVR extended posteriorly over the next month, after which the patient developed a tractional macula-off retinal detachment. Pars Plana vitrectomy was performed, confirming dense anterior and posterior PVR. A review of the literature suggests that an inflammatory cascade, similar to that seen in PVR development following rhegmatogenous retinal detachment, may occur from the destruction of the ciliary body by cyclodiode. As a result, fibrous transformation may occur, likely accounting for the cause of PVR development in this case. CONCLUSION: The pathophysiology of PVR development remains unclear. This case demonstrates that PVR may occur following cyclodiode and should be considered during postoperative monitoring after this procedure.

Performance of deep-learning artificial intelligence algorithms in detecting retinopathy of prematurity: A systematic review.

PURPOSE: Artificial intelligence (AI) offers considerable promise for retinopathy of prematurity (ROP) screening and diagnosis. The development of deep-learning algorithms to detect the presence of disease may contribute to sufficient screening, early detection, and timely treatment for this preventable blinding disease. This review aimed to systematically examine the literature in AI algorithms in detecting ROP. Specifically, we focused on the performance of deep-learning algorithms through sensitivity, specificity, and area under the receiver operating curve (AUROC) for both the detection and grade of ROP. METHODS: We searched Medline OVID, PubMed, Web of Science, and Embase for studies published from January 1, 2012, to September 20, 2021. Studies evaluating the diagnostic performance of deep-learning models based on retinal fundus images with expert ophthalmologists' judgment as reference standard were included. Studies which did not investigate the presence or absence of disease were excluded. Risk of bias was assessed using the QUADAS-2 tool. RESULTS: Twelve studies out of the 175 studies identified were included. Five studies measured the performance of detecting the presence of ROP and seven studies determined the presence of plus disease. The average AUROC out of 11 studies was 0.98. The average sensitivity and specificity for detecting ROP was 95.72% and 98.15%, respectively, and for detecting plus disease was 91.13% and 95.92%, respectively. CONCLUSION: The diagnostic performance of deep-learning algorithms in published studies was high. Few studies presented externally validated results or compared performance to expert human graders. Large scale prospective validation alongside robust study design could improve future studies.

Pulmonary Artery Pseudoaneurysms in the Setting of Gout Polyarthropathy: A Case Report.

Pulmonary artery pseudoaneurysm is a dangerous pathology often missed as a differential and on imaging. With a 50% mortality rate early detection and intervention are critical to patient outcome, rendering diagnosis based on clinical grounds to be of paramount importance. A high index of suspicion should be placed on patients presenting with unprovoked hemoptysis, hemorrhage or hemothorax. Knowledge of the common causes of pulmonary artery pseudoaneurysms is also important; however, it is integral to note spontaneous occurrences in patients with no known risk factors. Herein, we report a case of an unprovoked pulmonary artery pseudoaneurysm in a 72-year-old male with no known risk factors or causes for the pathology. Additionally, we review the pathophysiology behind the potential association of polyarthritic gout as a cause of pulmonary artery pseudoaneurysms. To the best of our knowledge this is the first reported link between the two diseases, providing grounds to widen literature and increase diagnoses of pulmonary artery pseudoaneurysms.